An analysis on credit risk
NU Bootcamp Module 21
Machine learning models for predicting credit risk in LendingClub dataset.
Unsupervised machine learning models used to group the cryptocurrencies to help prepare for a new investment.
The nonprofit foundation Alphabet Soup wants a tool that can help it select the applicants for funding with the best chance of success in their ventures
In this project we will work with housing data for the city of Ames, Iowa, United States from 2006 to 2010. You can read more about why the data was collected here (https://doi.org/10.1080/10691898.2011.11889627). You can also read about the different columns in the data here (https://www.tandfonline.com/doi/abs/10.1080/10691898.2011.11889627).
Nonprofit foundation Alphabet Soup wants a tool that can help it select the applicants for funding with the best chance of success in their ventures. Using machine learning and neural networks, you’ll use the features in the provided dataset to create a binary classifier that can predict whether applicants will be successful if funded.
Add a description, image, and links to the get-dummies topic page so that developers can more easily learn about it.
To associate your repository with the get-dummies topic, visit your repo's landing page and select "manage topics."